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This paper describes a method for robust real time pattern matching. We first introduce a family of image distance measures, the "Image Hamming Distance Family". Members of this family are robust to occlusion, small geometrical transforms, light changes and nonrigid deformations. We then present a novel Bayesian framework for sequential hypothesis testing on finite populations. Based on this framework, we design an optimal rejection/acceptance sampling algorithm. This algorithm quicklydoi:10.1109/tpami.2007.70794 pmid:18566496 fatcat:poftawktkvghzgroqwtbubgaru